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Relative contributions of the host genome, microbiome, and environment to the metabolic profile

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Authors

Kim, Kangjin; Lee, Yunhwan; Won, Sungho

Issue Date
2022-09
Publisher
한국유전학회
Citation
Genes & Genomics, Vol.44 No.9, pp.1081-1089
Abstract
Background Metabolic syndrome is as a well-known risk factor for cardiovascular disease, which is associated with both genetic and environmental factors. Recently, the microbiome composition has been shown to affect the development of metabolic syndrome. Thus, it is expected that the complex interplay among host genetics, the microbiome, and environmental factors could affect metabolic syndrome. Objective To evaluate the relative contributions of genetic, microbiome, and environmental factors to metabolic syndrome using statistical approaches. Methods Data from the prospective Korean Association REsource project cohort (N = 8476) were used in this study, including single-nucleotide polymorphisms, phenotypes and lifestyle factors, and the urine-derived microbial composition. The effect of each data source on metabolic phenotypes was evaluated using a heritability estimation approach and a prediction model separately. We further experimented with various types of metagenomic relationship matrices to estimate the phenotypic variance explained by the microbiome. Results With the heritability estimation, five of the 11 metabolic phenotypes were significantly associated with metagenome-wide similarity. We found significant heritability for fasting glucose (4.8%), high-density lipoprotein cholesterol (4.9%), waist-hip ratio (7.7%), and waist circumference (5.6%). Microbiome compositions provided more accurate estimations than genetic factors for the same sample size. In the prediction model, the contribution of each source to the prediction accuracy varied for each phenotype. Conclusion The effects of host genetics, the metagenome, and environmental factors on metabolic syndrome were minimal. Our statistical analysis suffers from a small sample size, and the measurement error is expected to be substantial. Further analysis is necessary to quantify the effects with better accuracy.
ISSN
1976-9571
URI
https://hdl.handle.net/10371/185609
DOI
https://doi.org/10.1007/s13258-022-01277-2
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